Constraints-Enhanced Resilient Estimation Against Random Sensor Failures and Colored Measurement Noise

This letter proposes a novel scheme for resilient estimation under random sensor failures and colored measurement noise, without requiring hardware-redundant designs or complex adaptive mechanisms. The key insight is to leverage prior constraints to increase information redundancy. Resilience against both disturbances is achieved through a Kalman-Petovello filter, which is derived by whitening the colored noise via measurement differencing and incorporating the prior constraints as pseudo measurements. Theoretical analysis and numerical simulations demonstrate the efficacy of the proposed scheme.